In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 10

Multi-objective adaptive particle swarm optimization algorithm with environment recognition and application

Wu Baotong1a,1b
Shu Ruoqi2a,2b
Chen Zhixiang1a,1b
1. a. School of Business, b. Laboratory of Big Data Driven Management Behavior & Decision Making, Sun Yat-sen University, Guangzhou Guangdong 510006, China
2. a. School of Intelligent Manufacturing, b. Zhaoqing City Key Laboratory of Advanced Manufacturing Technology & Equipment, Guangdong Technology College, Zhaoqing Guangdong 526100, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0090

Abstract

To address the issues of the standard multi-objective particle swarm optimization algorithm, such as getting trapped in local optima, overly fast convergence, and low precision during the optimization process, this paper proposed a multi-objective adaptive particle swarm algorithm based on environmental recognition. The initial population was generated using an optimal point set strategy to ensure individuals are uniformly distributed in the solution space. A nonlinear inertia weight mechanism and a crossover mutation strategy were employed to prevent the algorithm from converging too quickly during the search process. Additionally, an adaptive learning operator and an adaptive jump collaboration operator based on environmental recognition were introduced, which facilitate interaction and learning among particles by self-identifying the diversity level of the population in the solution space and the crowding degree within the particle's local niche. Comparative simulation experiments on multiple benchmark functions show that the improved algorithm significantly enhances both search capability and optimization precision. Finally, a practical multi-stage production case with NP-hard validates the effectiveness of algorithm.

Foundation Support

广东省自然科学基金资助项目(2025A1515010562)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0090
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 10

Publish History

[2025-06-18] Accepted Paper

Cite This Article

武保同, 舒若琦, 陈志祥. 基于环境识别策略的多目标自适应粒子群算法及应用 [J]. 计算机应用研究, 2025, 42 (10). (2025-06-19). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0090. (Wu Baotong, Shu Ruoqi, Chen Zhixiang. Multi-objective adaptive particle swarm optimization algorithm with environment recognition and application [J]. Application Research of Computers, 2025, 42 (10). (2025-06-19). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0090. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)